This notebook describes some early exploration into data downloaded from the Glooko website. It includes both Dexcom CGM readings as well as Omnipod 5 insulin pump information.
My daughter was diagnosed in November and is still well within the diabetes "honeymoon" phase. The honeymoon phase happens after a Type 1 diabetic first starts receiving insulin. For some reason, the pancreas decides to kick into gear again, producing some of its own insulin in decreasing and unpredictable quantities. This can last from a few months to a few years. All of this means that it's very unlikely I'll find much in the way of insightful correlations. But I still want to look.
I am curious about smaller, short-term patterns. Some initial questions I have include:
But probably the biggest questions are:
import pandas as pd
import numpy as np
import hvplot.pandas #noqa
def read_all(data_folder):
"""
Read all of the data and return a series of pandas data frames
Example usage:
df_cgm, df_bolus, df_basal, df_insulin = read_all(r"data")
Args:
data_folder (str): path to the root folder containing all the data files. The file names
and structure should be left exactly as they were in the initial download from Glooko.
Returns:
A 4-element tuple
- **df_cgm** (pandas df): pandas dataframe containing CGM time series data
- **df_bolus** (pandas df): Bolus data
- **df_basal** (pandas df): Basal data
- **df_insulin** (pandas df): Insulin corrections data
"""
# Load and format CGM data
df_cgm_data = pd.read_csv(data_folder + r"/cgm_data.csv", header=1,\
names=["time", "bg", "sn_cgm"])
df_cgm_data["time"] = pd.to_datetime(df_cgm_data["time"])
# Load and format bolus data
df_bolus_data = pd.read_csv(data_folder + r"/Insulin data/bolus_data.csv", header=1, \
names=["time", "insulin_type", "bg_input", "carbs_input", "carb_ratio",\
"insulin_delivered", "initial_delivery", "extended_delivery", "sn_omni"])
df_bolus_data["time"] = pd.to_datetime(df_bolus_data["time"])
# Load and format basal data
df_basal_data = pd.read_csv(data_folder + r"/Insulin data/basal_data.csv", header=1,\
names=["time", "insulin_type", "duration", "percentage", "rate", "insulin_delivered",\
"sn_omni"])
df_basal_data["time"] = pd.to_datetime(df_basal_data["time"])
# Load and format insulin data
df_insulin_data = pd.read_csv(data_folder + r"/Insulin data/insulin_data.csv", header=1,\
names=["time", "total_bolus", "total_insulin", "total_basal", "sn_omni"])
df_insulin_data["time"] = pd.to_datetime(df_insulin_data["time"])
return df_cgm_data, df_bolus_data, df_basal_data, df_insulin_data
# Load all of the data into pandas dataframes
df_cgm, df_bolus, df_basal, df_insulin = read_all(r"data")
Everything I want to look at initially I can get from the Bolus data file. To start with, the bolus sheet includes the total insulin delivered, but you don't explicitly know how much was due to the insulin correction and how much was due to carbs. I will initially calculate by simply computing the carb dose using the carb ratio and carbs input. I'll then find the difference between that and the insulin deivered to get the portion that was a "glucose correction". This is actually a little more complex because there are additional factors like insulin-on-board (IOB).
df_bolus["carb_correction"] = np.divide(df_bolus["carbs_input"], df_bolus["carb_ratio"])
df_bolus["insulin_correction"] = df_bolus["insulin_delivered"] - df_bolus["carb_correction"]
df_cgm.hvplot.line(x='time', y='bg', ylabel='Blood glucose (mg/dL)', xlabel='Time', height=500, width=620, color='lightgray')*\
df_cgm.hvplot.scatter(x='time', y='bg')